OpenRaiser/Intern-Atlas huggingface dataset:
Ion Transmitter for Molecular Communication
Venue: arXiv cs.ET 2024
Year: 2024

Molecular communication (MC) is an emerging paradigm that takes inspiration from biological processes, enabling communication at the nanoscale and facilitating the development of the Internet of Bio-Nano Things (IoBNT). Traditional models of MC often rely on idealized assumptions that overlook practical challenges related to noise and signal behavior. This paper proposes and evaluates the first physical MC ion transmitter (ITX) using an ion exchange membrane. The circuit network model is used to simulate ion transport and analyze both transient and steady-state behavior. This analysis includes the effects of noise sources such as thermal and shot noise on signal integrity and SNR. The main contributions of this paper are to demonstrate how a practical MC ITX can produce a realistic waveform and to highlight future research challenges associated with a physical membrane-based ITX.
One-Time Signature Based on Pseudorandom Number Generator
Venue: arXiv cs.CR 2024
Year: 2024

With the advancement of quantum computing technologies, recent years have seen increasing efforts to identify cryptographic methods resistant to quantum attacks and to establish post-quantum cryptography (PQC) approaches. Among these, hash-based digital signature algorithms (DSAs) are a notable category of PQC. Hash functions are not only utilized in digital signatures but are also widely applied in pseudorandom number generators (PRNGs). Building on the foundation of hash-based DSAs, this study proposes a modified approach that introduces a DSA based on PRNGs, suitable for one-time signature (OTS) applications. The study explores the security of the proposed PRNG-based OTS algorithm and validates its feasibility through experiments comparing various parameter configurations. These experiments examine key length, signature length, key generation time, signature generation time, and signature verification time under different parameter settings.
The Continuous Logarithm in the Complex Circle for Post-Quantum Cryptographic Algorithms
Venue: arXiv cs.CR 2024
Year: 2024

This paper introduces a novel cryptographic approach based on the continuous logarithm in the complex circle, designed to address the challenges posed by quantum computing. By leveraging its multi-valued and spectral properties, this framework enables the reintroduction of classical algorithms (DH, ECDSA, ElGamal, EC) and elliptic curve variants into the post-quantum landscape. Transitioning from classical or elliptic algebraic structures to the geometric and spectral properties of the complex circle, we propose a robust and adaptable foundation for post-quantum cryptography.
Towards General Purpose Robots at Scale: Lifelong Learning and Learning to Use Memory
Venue: arXiv cs.LG 2024
Year: 2024

The widespread success of artificial intelligence in fields like natural language processing and computer vision has not yet fully transferred to robotics, where progress is hindered by the lack of large-scale training data and the complexity of real-world tasks. To address this, many robot learning researchers are pushing to get robots deployed at scale in everyday unstructured environments like our homes to initiate a data flywheel. While current robot learning systems are effective for certain short-horizon tasks, they are not designed to autonomously operate over long time horizons in unstructured environments. This thesis focuses on addressing two key challenges for robots operating over long time horizons: memory and lifelong learning. We propose two novel methods to advance these capabilities. First, we introduce t-DGR, a trajectory-based deep generative replay method that achieves state-of-the-art performance on Continual World benchmarks, advancing lifelong learning. Second, we develop a framework that leverages human demonstrations to teach agents effective memory utilization, improving learning efficiency and success rates on Memory Gym tasks. Finally, we discuss future directions for achieving the lifelong learning and memory capabilities necessary for robots to function at scale in real-world settings.
Accelerating End-Cloud Collaborative Inference via Near Bubble-free Pipeline Optimization
Venue: arXiv cs.DC 2024
Year: 2024

End-cloud collaboration offers a promising strategy to enhance the Quality of Service (QoS) in DNN inference by offloading portions of the inference workload from end devices to cloud servers. Despite the potential, the complex model architectures and dynamic network conditions will introduce numerous bubbles (\ie, idle waiting time) in pipeline execution, resulting in inefficient resource utilization and degraded QoS. To address these challenges, we introduce a novel framework named COACH, designed for near bubble-free pipeline collaborative inference, thereby achieving low inference latency and high system throughput. Initially, COACH employs an \textit{offline} component that utilizes an efficient recursive divide-and-conquer algorithm to optimize both model partitioning and transmission quantization, aiming to minimize the occurrence of pipeline bubbles. Subsequently, the \textit{online} component in COACH employs an adaptive quantization adjustment and a context-aware caching strategy to further stabilize pipeline execution. Specifically, COACH analyzes the correlation between intermediate data and label semantic centers in the cache, along with its influence on the quantization adjustment, thereby effectively accommodating network fluctuations. Our experiments demonstrate the efficacy of COACH in reducing inference latency and enhancing system throughput. Notably, while maintaining comparable accuracy, COACH achieves up to 1.7x faster inference and 2.1x higher system throughput than baselines.
From Critique to Clarity: A Pathway to Faithful and Personalized Code Explanations with Large Language Models
Venue: arXiv cs.SE 2024
Year: 2024

In the realm of software development, providing accurate and personalized code explanations is crucial for both technical professionals and business stakeholders. Technical professionals benefit from enhanced understanding and improved problem-solving skills, while business stakeholders gain insights into project alignments and transparency. Despite the potential, generating such explanations is often time-consuming and challenging. This paper presents an innovative approach that leverages the advanced capabilities of large language models (LLMs) to generate faithful and personalized code explanations. Our methodology integrates prompt enhancement, self-correction mechanisms, personalized content customization, and interaction with external tools, facilitated by collaboration among multiple LLM agents. We evaluate our approach using both automatic and human assessments, demonstrating that our method not only produces accurate explanations but also tailors them to individual user preferences. Our findings suggest that this approach significantly improves the quality and relevance of code explanations, offering a valuable tool for developers and stakeholders alike.
ARCEAK: An Automated Rule Checking Framework Enhanced with Architectural Knowledge
Venue: arXiv cs.SE 2024
Year: 2024

Automated Rule Checking (ARC) plays a crucial role in advancing the construction industry by addressing the laborious, inconsistent, and error-prone nature of traditional model review conducted by industry professionals. Manual assessment against intricate sets of rules often leads to significant project delays and expenses. In response to these challenges, ARC offers a promising solution to improve efficiency and compliance in design within the construction sector. However, the main challenge of ARC lies in translating regulatory text into a format suitable for computer processing. Current methods for rule interpretation require extensive manual labor, thereby limiting their practicality. To address this issue, our study introduces a novel approach that decomposes ARC into two distinct tasks: rule information extraction and verification code generation. Leveraging generative pre-trained transformers, our method aims to streamline the interpretation of regulatory texts and simplify the process of generating model compliance checking code. Through empirical evaluation and case studies, we showcase the effectiveness and potential of our approach in automating code compliance checking, enhancing the efficiency and reliability of construction projects.
On Design Choices in Similarity-Preserving Sparse Randomized Embeddings
Venue: arXiv cs.NE 2024
Year: 2024

Expand & Sparsify is a principle that is observed in anatomically similar neural circuits found in the mushroom body (insects) and the cerebellum (mammals). Sensory data are projected randomly to much higher-dimensionality (expand part) where only few the most strongly excited neurons are activated (sparsify part). This principle has been leveraged to design a FlyHash algorithm that forms similarity-preserving sparse embeddings, which have been found useful for such tasks as novelty detection, pattern recognition, and similarity search. Despite its simplicity, FlyHash has a number of design choices to be set such as preprocessing of the input data, choice of sparsifying activation function, and formation of the random projection matrix. In this paper, we explore the effect of these choices on the performance of similarity search with FlyHash embeddings. We find that the right combination of design choices can lead to drastic difference in the search performance.
KVDirect: Distributed Disaggregated LLM Inference
Venue: arXiv cs.DC 2024
Year: 2024

Large Language Models (LLMs) have become the new foundation for many applications, reshaping human society like a storm. Disaggregated inference, which separates prefill and decode stages, is a promising approach to improving hardware utilization and service quality. However, due to inefficient inter-node communication, existing systems restrict disaggregated inference to a single node, limiting resource allocation flexibility and reducing service capacity. This paper introduces KVDirect, which optimizes KV cache transfer to enable a distributed disaggregated LLM inference. KVDirect achieves this through the following contributions. First, we propose a novel tensor-centric communication mechanism that reduces the synchronization overhead in traditional distributed GPU systems. Second, we design a custom communication library to support dynamic GPU resource scheduling and efficient KV cache transfer. Third, we introduce a pull-based KV cache transfer strategy that reduces GPU resource idling and improves latency. Finally, we implement KVDirect as an open-source LLM inference framework. Our evaluation demonstrates that KVDirect reduces per-request latency by 55% compared to the baseline across diverse workloads under the same resource constraints.
Neuromorphic Spiking Neural Network Based Classification of COVID-19 Spike Sequences
Venue: arXiv cs.NE 2024
Year: 2024

The availability of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus data post-COVID has reached exponentially to an enormous magnitude, opening research doors to analyze its behavior. Various studies are conducted by researchers to gain a deeper understanding of the virus, like genomic surveillance, etc, so that efficient prevention mechanisms can be developed. However, the unstable nature of the virus (rapid mutations, multiple hosts, etc) creates challenges in designing analytical systems for it. Therefore, we propose a neural network-based (NN) mechanism to perform an efficient analysis of the SARS-CoV-2 data, as NN portrays generalized behavior upon training. Moreover, rather than using the full-length genome of the virus, we apply our method to its spike region, as this region is known to have predominant mutations and is used to attach to the host cell membrane. In this paper, we introduce a pipeline that first converts the spike protein sequences into a fixed-length numerical representation and then uses Neuromorphic Spiking Neural Network to classify those sequences. We compare the performance of our method with various baselines using real-world SARS-CoV-2 spike sequence data and show that our method is able to achieve higher predictive accuracy compared to the recent baselines.
Integration of IoT- AI powered local weather forecasting: A Game-Changer for Agriculture
Venue: arXiv cs.OH 2024
Year: 2024

The dynamic environment context necessitates harnessing digital technologies, including artificial intelligence and the Internet of Things, to supply high-resolution, real-time meteorological data to support agricultural decision-making and improve overall farm productivity and sustainability. This study investigates the potential application of various AI-powered, IoT-based, low-cost platforms for local weather forecasting to enable smart farming. Despite the increasing demand for this topic, a few promising studies have explored this area. This paper developed a conceptual research framework based on a systematic review of relevant literature and employed a case study method to validate the framework. The framework comprised five key components: the Data Acquisition Layer, Data Storage Layer, Data Processing Layer, Application Layer, and Decision-Making Layer. This paper contributes to the literature by exploring the integration of AI-ML and IoT techniques for weather prediction tasks to support agriculture, and the incorporation of IoT technologies that provide real-time, high-resolution meteorological data, representing a step forward. Furthermore, this paper discusses key research gaps, such as the significant obstacles impeding the adoption of AI in agriculture and local weather forecasting, including the lack of straightforward solutions and the lack of digital skills among farmers, particularly those in rural areas. Further empirical research is needed to enhance the existing frameworks and address these challenges.
Towards An Automated AI Act FRIA Tool That Can Reuse GDPR's DPIA
Venue: arXiv cs.CY 2024
Year: 2024

The AI Act introduces the obligation to conduct a Fundamental Rights Impact Assessment (FRIA), with the possibility to reuse a Data Protection Impact Assessment (DPIA), and requires the EU Commission to create of an automated tool to support the FRIA process. In this article, we provide our novel exploration of the DPIA and FRIA as information processes to enable the creation of automated tools. We first investigate the information involved in DPIA and FRIA, and then use this to align the two to state where a DPIA can be reused in a FRIA. We then present the FRIA as a 5-step process and discuss the role of an automated tool for each step. Our work provides the necessary foundation for creating and managing information for FRIA and supporting it through an automated tool as required by the AI Act.
Hybrid Cooperative Co-Evolution Algorithm for Deadlock-prone Distributed Assembly Flowshop Scheduling with Limited buffers Using Petri nets
Venue: arXiv cs.DC 2024
Year: 2024

The distributed assembly flowshop scheduling problem (DAFSP) can be applied to immense manufacturing environments. In DAFSP, jobs are first processed in distributed flowshops, and then assembled into final products by an assembly machine, which usually has limited buffers in practical application. This limited capacity can lead to deadlocks, halting job completion and blocking the entire manufacturing process. However, existing scheduling methods fail to address these deadlocks in DAFSP effectively. As such, we develop a hybrid cooperative co-evolution (HCCE) algorithm for solving the deadlock-prone DAFSP by minimizing the makespan. For the first time, we use Petri nets to analyze the deadlocks in DAFSP and propose a Petri net-based deadlock amending method (IDAM), which is further integrated into HCCE to ensure the feasibility (i.e., deadlock-freeness) of solutions. Importantly, HCCE contains an elite archive (EAR) and two subpopulations. It uses the problem-specific operators for heuristic initialization and global-search. To enhance the quality and diversity of solutions, an information transfer mechanism (ITM) is developed among subpopulation and EAR, and four local-search operators are performed sequentially on each individual in EAR. Finally, comprehensive experiments demonstrate the effectiveness and superiority of the proposed HCCE algorithm.
Good Practices for Institutional Organization of Research Institutes: Excellence in Research and Positive Impact on Society
Venue: arXiv cs.CY 2024
Year: 2024

In this paper, we analyze examples of research institutes that stand out in scientific excellence and social impact. We define key practices for evaluating research results, economic conditions, and the selection of specific research topics. Special focus is placed on small countries and the field of artificial intelligence. The aim is to identify components that enable institutes to achieve a high level of innovation, self-sustainability, and social benefits.
Reconstructing 3D Flow from 2D Data with Diffusion Transformer
Venue: arXiv cs.CE 2024
Year: 2024

Fluid flow is a widely applied physical problem, crucial in various fields. Due to the highly nonlinear and chaotic nature of fluids, analyzing fluid-related problems is exceptionally challenging. Computational fluid dynamics (CFD) is the best tool for this analysis but involves significant computational resources, especially for 3D simulations, which are slow and resource-intensive. In experimental fluid dynamics, PIV cost increases with dimensionality. Reconstructing 3D flow fields from 2D PIV data could reduce costs and expand application scenarios. Here, We propose a Diffusion Transformer-based method for reconstructing 3D flow fields from 2D flow data. By embedding the positional information of 2D planes into the model, we enable the reconstruction of 3D flow fields from any combination of 2D slices, enhancing flexibility. We replace global attention with window and plane attention to reduce computational costs associated with higher dimensions without compromising performance. Our experiments demonstrate that our model can efficiently and accurately reconstruct 3D flow fields from 2D data, producing realistic results.
Hgformer: Hyperbolic Graph Transformer for Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effective method to alleviate the cold start problem. However, the modelling distortion for long-tail data, which is widely present in recommender systems, is often overlooked in cross-domain recommendation. In this research, we propose a hyperbolic manifold based cross-domain collaborative filtering model using BiTGCF as the base model. We introduce the hyperbolic manifold and construct new propagation layer and transfer layer to address these challenges. The significant performance improvements across various datasets compared to the baseline models demonstrate the effectiveness of our proposed model.
GenAIOps for GenAI Model-Agility
Venue: arXiv cs.SE 2024
Year: 2024

AI-agility, with which an organization can be quickly adapted to its business priorities, is desired even for the development and operations of generative AI (GenAI) applications. Especially in this paper, we discuss so-called GenAI Model-agility, which we define as the readiness to be flexibly adapted to base foundation models as diverse as the model providers and versions. First, for handling issues specific to generative AI, we first define a methodology of GenAI application development and operations, as GenAIOps, to identify the problem of application quality degradation caused by changes to the underlying foundation models. We study prompt tuning technologies, which look promising to address this problem, and discuss their effectiveness and limitations through case studies using existing tools.
Assessment of FAIR (Findability, Accessibility, Interoperability, and Reusability) data implementation frameworks: a parametric approach
Venue: arXiv cs.DL 2024
Year: 2024

Open science movement has established reproducibility, transparency, and validation of research outputs as essential norms for conducting scientific research. It advocates for open access to research outputs, especially research data, to enable verification of published findings and its optimum reuse. The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles support the philosophy of open science and have emerged as a foundational framework for making digital assets machine-actionable and enhancing their reusability and value in various domains, particularly in scientific research and data management. In response to the growing demand for making data FAIR, various FAIR implementation frameworks have been developed by various organizations to educate and make the scientific community more aware of FAIR and its principles and to make the adoption and implementation of FAIR easier. This paper provides a comprehensive review of the openly available FAIR implementation frameworks based on a parametric evaluation of these frameworks. The current work identifies 13 frameworks and compares them against their coverage of the four foundational principles of FAIR, including an assessment of these frameworks against 36 parameters related to technical specifications, basic features, and FAIR implementation features and FAIR coverage. The study identifies that most of the frameworks only offer a step-by-step guide to FAIR implementation and seem to be adopting the technology-first approach, mostly guiding the deployment of various tools for FAIR implementation. Many frameworks are missing the critical aspects of explaining what, why, and how for the four foundational principles of FAIR, giving less consideration to the social aspects of FAIR. The study concludes that more such frameworks should be developed, considering the people-first approach rather than the technology-first.
A variational and symplectic framework for model-free control: preliminary results
Venue: arXiv cs.SY 2025
Year: 2025

The model-free control approach is an advanced control law that requires few information about the process to control. Since its introduction in 2008, numerous applications have been successfully considered, highlighting attractive robustness properties towards tracking efficiency and disturbance rejection. In this work, a variational approach of the model-free control is proposed in order to extend its robustness capabilities. An adaptive formulation of the controller is proposed using the calculus of variations within a symplectic framework, that aims to consider the control law as an optimization problem toward the auto-tuning of its main key parameter. The proposed formulation provides a coupling between the model-free control law and a variational integrator to improve the robustness of the tracking towards process changes and emphasize closed-loop stabilization. Some illustrative examples are discussed to highlight the rightness of the proposed approach.
Applications of Quantified Constraint Solving over the Reals -- Bibliography
Venue: arXiv cs.LO 2025
Year: 2025

Quantified constraints over the reals appear in numerous contexts. Usually existential quantification occurs when some parameter can be chosen by the user of a system, and univeral quantification when the exact value of a parameter is either unknown, or when it occurs in infinitely many, similar versions. The following is a list of application areas and publications that contain applications for solving quantified constraints over the reals. The list is certainly not complete, but grows as the author encounters new items. Contributions are very welcome!